{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import config\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import ltech\n",
    "from lte.phy import Phy\n",
    "l = ltech.LTE(4)\n",
    "for cid in [84]:\n",
    "    for port_id in range(2):\n",
    "        l.ioc.append(l.refs[cid].cs_table(port_id))\n",
    "l.start()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 采集100秒数据，只关心Tx1和Tx0的相对关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = 10000\n",
    "r = np.zeros((time,128,4,2),dtype=complex)\n",
    "l.start()\n",
    "for i in range(time):\n",
    "    res,t = l.run()\n",
    "    r[i,:,:,:] = res\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出复相对幅度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for i,color in enumerate(\"rgby\"):\n",
    "    plt.subplot(221+i)\n",
    "    plt.plot(r[:,64,i,0].real,color)\n",
    "    plt.plot(r[:,64,i,0].imag,color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name '画出相位变化' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-ccdf7b74c800>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m画出相位变化\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name '画出相位变化' is not defined"
     ]
    }
   ],
   "source": [
    "画出相位变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,color in enumerate(\"rgby\"):\n",
    "    plt.subplot(221+i)\n",
    "    plt.plot(np.angle(r[:,i]),color)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}